Abstract
By using the particle-based code Gadget2, we follow the evolution of a gas giant molecular cloud, in which a set of gas particles representing the wind are created by a Monte Carlo scheme and suddenly move outwards from the cloud’s center. The particles representing the gas cloud initially have a velocity according to a turbulent spectrum built in a Fourier space of 643grid elements. The level of turbulence and the temperature of the cloud are both adjusted so that a gravitational collapse of the cloud is initially induced. All the winds are activated in a very early stage of evolution of the cloud. We consider only two kinds of winds, namely, one with spherical symmetry and the second one of a bipolar collimated jet. In order to assess the dynamical change in the cloud due to interactions with the winds, we show isovelocity and isodensity plots for all our simulations. We also report on the accretion centers detected at the last simulation time available for each model.
Highlights
Stars are born in large gas structures made of molecular hydrogen
The physical process by which the molecular gas is transformed from a gas structure into stars is mainly gravitational collapse, whose main effect on the cloud is a reduction in size with the corresponding increase in density at various points of the cloud
The basic idea of prompt fragmentation is that during gravitational collapse a molecular cloud may spontaneously break into several cores
Summary
Stars are born in large gas structures made of molecular hydrogen. According to [1] a cloud is a gas structure with mass and size around 1000–10000 M⊙ and 2–15 pc, respectively. The physical process by which the molecular gas is transformed from a gas structure into stars is mainly gravitational collapse, whose main effect on the cloud is a reduction in size with the corresponding increase in density at various points of the cloud. These small overdensities within the larger gas structure are defined as cores, which have a typical mass and size around 0.5–5 M⊙ and 0.03–0.2 pc, respectively, [1]. As we use here the fully parallelized Gadget code which implements the SPH technique, this will make it possible to carry out an interesting comparison between the two computational techniques
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